The invention relates to a computer-implemented method for training a neural network comprising a neural ordinary differential equation (ODE) block. A first ODE solver may be used to train the neural ODE block. A second ODE solver may be used to train and verify that the neural ODE block describes an ODE as an ODE flow. During a forward pass of an iteration of training, a first performance value is obtained by applying the first ODE solver to the neural ODE block and a second performance value is obtained by applying the second ODE solver to the neural ODE block. An accuracy parameter of the first ODE solver is adjusted based on the difference between the first performance value and the second performance value.
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